Information processing system, information processing method, information processing apparatus, control method therefor, and storage medium storing control program therefor

ABSTRACT

This invention relates to an information processing apparatus which assists diagnosis based on a tissue sample image obtained by staining and capturing a tissue. The information processing apparatus receives and analyzes lower magnification image data among a plurality of image data obtained at different magnifications for an area image selected in the tissue sample image. Based on the analysis result, the information processing apparatus determines whether analysis based on higher magnification image data is necessary. When analysis based on the higher magnification image data is necessary, the information processing apparatus notifies a request of transmitting the higher magnification image data for the area image, receives and analyzes the higher magnification image data transmitted in response to the transmission request, and transmits the analysis result. This arrangement can quickly provide high-accuracy diagnosis assistance for a tissue sample image from a pathologist regardless of the restriction of the transmission capacity.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Divisional of U.S. patent application Ser. No.13/981,275, filed on Jul. 23, 2013, which is a National Stage ofInternational Application No. PCT/JP2012/050249, filed on Jan. 10, 2012,which claims priority from Japanese Patent Application No. 2011-012425filed Jan. 24, 2011, the contents of all of which are incorporatedherein by reference in their entirety.

TECHNICAL FIELD

The present invention relates to an information processing technique forassisting diagnosis based on the tissue sample image of a tissue.

BACKGROUND ART

In a technique for assisting diagnosis based on the tissue sample imageof a tissue, for example, a cancer cell region is selected from a tissuesample image to analyze features such as the number of cancer cells andprovide them to a pathologist. For example, patent literature 1discloses a diagnosis assistance system which transmits a medical imagefrom a medical image forming system 12 to a specialist in a remoteobservation station 26, and receives assistance of a diagnosis by him.

CITATION LIST Patent Literature

Patent literature 1: Japanese PCT National Publication No. 2004-500211(WO2001/075776)

SUMMARY OF THE INVENTION Technical Problem

However, when the technique in patent literature 1 is applied to adiagnosis assistance system in which many pathologists request, of adiagnostic center, diagnosis assistance of tissue sample images, theyhave to wait for the replies of analysis results for a long time becausetransmission of tissue sample images takes time under the restriction ofthe transmission capacity. For example, transmitting the tissue sampleimage of one slide using a general public line sometimes takes severalmin to 10 min or longer.

The present invention enables to provide a technique of solving theabove-described problem.

Solution to Problem

One aspect of the present invention provides an information processingapparatus which assists diagnosis based on a tissue sample imageobtained by staining and capturing a tissue, comprising a first receiverthat receives lower-magnification image data among a plurality of imagedata obtained at different magnifications for an area image selected inthe tissue sample image;

a first analyzer that analyzes the area image based on thelower-magnification image data received by said first receiver, andgenerates first feature information;

a determination unit that determines whether analysis based onhigher-magnification image data is necessary for the area image, basedon the first feature information generated by said first analyzer;

a notification unit that notifies a request of transmitting thehigher-magnification image data for the area image, when saiddetermination unit determines that analysis based on thehigher-magnification image data is necessary;

a second receiver that receives the higher-magnification image datatransmitted in response to the transmission request from saidnotification unit;

a second analyzer that analyzes the area image based on thehigher-magnification image data received by said second receiver, andgenerates second feature information; and

a transmitter that transmits the second feature information generated bysaid second analyzer.

Another aspect of the present invention provides a method forcontrolling an information processing apparatus which assists diagnosisbased on a tissue sample image obtained by staining and capturing atissue, comprising

a first receiving step of receiving lower magnification image data amonga plurality of image data obtained at different magnifications for anarea image selected in the tissue sample image;

a first analyzing step of analyzing the area image based on the lowermagnification image data received in said first receiving step, andgenerating first feature information;

a determination step of determining whether analysis based on highermagnification image data is necessary for the area image, based on thefirst feature information generated in the first analyzing step;

a notification step of notifying a request of transmitting the highermagnification image data for the area image, when analysis based on thehigher magnification image data is determined to be necessary in saiddetermination step;

a second receiving step of receiving the higher magnification image datatransmitted in response to the transmission request in said notificationstep;

a second analyzing step of analyzing the area image based on the highermagnification image data received in the second receiving step, andgenerating second feature information; and

a transmitting step of transmitting the second feature informationgenerated in the second analyzing step.

Still other aspect of the present invention provides a non-transitorycomputer-readable storage medium storing a program for controlling aninformation processing apparatus which assists diagnosis based on atissue sample image obtained by staining and capturing a tissue, thecontrol program causing a computer to execute

a first receiving step of receiving lower magnification image data amonga plurality of image data obtained at different magnifications for anarea image selected in the tissue sample image;

a first analyzing step of analyzing the area image based on the lowermagnification image data received in said first receiving step, andgenerating first feature information;

a determination step of determining whether analysis based on highermagnification image data is necessary for the area image, based on thefirst feature information generated in the first analyzing step;

a notification step of notifying a request of transmitting the highermagnification image data for the area image, when analysis based on thehigher magnification image data is determined to be necessary in saiddetermination step;

a second receiving step of receiving the higher magnification image datatransmitted in response to the transmission request in said notificationstep;

a second analyzing step of analyzing the area image based on the highermagnification image data received in the second receiving step, andgenerating second feature information; and

a transmitting step of transmitting the second feature informationgenerated in the second analyzing step.

Still other aspect of the present invention provides an informationprocessing apparatus which requests assistance of diagnosis based on atissue sample image obtained by staining and capturing a tissue,comprising

a first transmitter that transmits lower magnification image data amonga plurality of image data obtained at different magnifications for anarea image selected in the tissue sample image, in association withtransmission source identifying information for identifying theinformation processing apparatus, and image data identifying informationfor identifying the image data;

a second transmitter that transmits, in response to a notification of arequest of transmitting higher magnification image data among theplurality of image data obtained at different magnifications, the highermagnification image data for the area image in association with thetransmission source identifying information and the image dataidentifying information;

a receiver that receives feature information of the area imageassociated with the image data identifying information; and

a display unit that displaying presence/absence information of thenotification of the transmission request for the area image, and thefeature information of the area image with distinguishably superimposingthem on the tissue sample image.

Still other aspect of the present invention provides a method forcontrolling an information processing apparatus which requestsassistance of diagnosis based on a tissue sample image obtained bystaining and capturing a tissue, comprising

a first transmitting step of transmitting lower magnification image dataamong a plurality of image data obtained at different magnifications foran area image of an area selected in the tissue sample image, inassociation with transmission source identifying information foridentifying the information processing apparatus, and image dataidentifying information for identifying the image data;

a second transmitting step of transmitting, in response to anotification of a request to transmit higher magnification image dataamong the plurality of image data obtained at different magnifications,the higher magnification image data for the area image in associationwith the transmission source identifying information and the image dataidentifying information;

a receiving step of receiving feature information of the area imageassociated with the image data identifying information; and

a displaying step of displaying presence/absence of the notification ofthe transmission request for the area image, and the feature informationof the area image with distinguishably superimposing them on the tissuesample image.

Still other aspect of the present invention provides a non-transitorycomputer-readable storage medium storing a program for controlling aninformation processing apparatus which requests assistance of diagnosisbased on a tissue sample image obtained by staining and capturing atissue, the control program causing a computer to execute

a first transmitting step of transmitting lower magnification image dataamong a plurality of image data obtained at different magnifications foran area image of an area selected in the tissue sample image, inassociation with transmission source identifying information foridentifying the information processing apparatus, and image dataidentifying information for identifying the image data;

a second transmitting step of transmitting, in response to anotification of a request to transmit higher magnification image dataamong the plurality of image data obtained at different magnifications,the higher magnification image data for the area image in associationwith the transmission source identifying information and the image dataidentifying information;

a receiving step of receiving feature information of the area imageassociated with the image data identifying information; and

a displaying step of displaying presence/absence of the notification ofthe transmission request for the area image, and the feature informationof the area image with distinguishably superimposing them on the tissuesample image.

Still other aspect of the present invention provides an informationprocessing system which assists diagnosis based on a tissue sample imageobtained by staining and capturing a tissue, comprising

a first analyzer that analyzes lower magnification image data among aplurality of image data obtained at different magnifications for an areaimage selected in the tissue sample image, and generates first featureinformation of the area image;

a determination unit that determines whether analysis of highermagnification image data is necessary for the area image, based on thefirst feature information generated by said first analyzer;

a second analyzer that analyzes the area image based on the highermagnification image data, and generates second feature information, whensaid determination unit determines that analysis of the highermagnification image data is necessary; and

a display unit that distinguishably displays a result of thedetermination by said determination unit and the second featureinformation generated by said second analyzer.

Still other aspect of the present invention provides an informationprocessing method for assisting diagnosis based on a tissue sample imageobtained by staining and capturing a tissue, comprising

a first analyzing step of analyzing lower magnification image data amonga plurality of image data obtained at different magnifications for thearea image selected in the tissue sample image, and generating firstfeature information of the area image;

a determination step of determining whether analysis of highermagnification image data is necessary for the area image, based on thefirst feature information generated in said first analyzing step;

a second analyzing step of analyzing the area image based on the highermagnification image data, and generating second feature information,when analysis of the higher magnification image data is determined to benecessary in said determination step; and

distinguishably displaying a result of the determination in saiddetermination step and the second feature information generated in saidsecond analyzing step.

Advantageous Effects of Invention

According to the present invention, high-accuracy diagnosis assistancecan be quickly provided for a tissue sample image from a pathologistregardless of the restriction of the transmission capacity.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing the arrangement of an informationprocessing apparatus according to the first embodiment of the presentinvention;

FIG. 2 is a block diagram showing the arrangement of an informationprocessing system including an information processing apparatusaccording to the second embodiment of the present invention;

FIG. 3 is a sequence chart showing the operation sequence of theinformation processing system including the information processingapparatus according to the second embodiment of the present invention;

FIG. 4 is a view showing a display screen when transmitting an ROI imagefrom a pathologist terminal according to the second embodiment of thepresent invention;

FIG. 5A is a view showing an analysis result display screen on thepathologist terminal according to the second embodiment of the presentinvention;

FIG. 5B is a view showing an analysis result display screen on thepathologist terminal according to the second embodiment of the presentinvention;

FIG. 6 is a block diagram showing the hardware arrangement of theinformation processing apparatus according to the second embodiment ofthe present invention;

FIG. 7A is a chart showing the structure of a low-magnification imagetable according to the second embodiment of the present invention;

FIG. 7B is a chart showing the structure of a high-magnification imagetable according to the second embodiment of the present invention;

FIG. 8A is a chart showing the structure of a tissue structure analysisDB according to the second embodiment of the present invention;

FIG. 8B is a chart showing the structure of a feature analysis DBaccording to the second embodiment of the present invention;

FIG. 9 is a flowchart showing the processing procedure of theinformation processing apparatus according to the second embodiment ofthe present invention;

FIG. 10 is a block diagram showing the hardware arrangement of thepathologist terminal according to the second embodiment of the presentinvention;

FIG. 11 is a chart showing the structures of an image identificationtable and transmission/reception data according to the second embodimentof the present invention;

FIG. 12 is a flowchart showing the processing procedure of thepathologist terminal according to the second embodiment of the presentinvention;

FIG. 13 is a sequence chart showing the operation sequence of aninformation processing system including an information processingapparatus according to the third embodiment of the present invention;

FIG. 14 is a block diagram showing the arrangement of an informationprocessing system including an information processing apparatusaccording to the fourth embodiment of the present invention;

FIG. 15 is a sequence chart showing the operation sequence of theinformation processing system including the information processingapparatus according to the fourth embodiment of the present invention;

FIG. 16 is a view showing a display screen when transmitting a diagnosisresult from a pathologist terminal according to the fourth embodiment ofthe present invention;

FIG. 17 is a view showing an analysis result display screen on thepathologist terminal according to the fourth embodiment of the presentinvention;

FIG. 18 is a block diagram showing the hardware arrangement of theinformation processing apparatus according to the fourth embodiment ofthe present invention;

FIG. 19 is a chart showing the structure of a diagnosis case DBaccording to the fourth embodiment of the present invention;

FIG. 20 is a flowchart showing the processing procedure of theinformation processing apparatus according to the fourth embodiment ofthe present invention;

FIG. 21 is a chart showing the structure of a patient history DBaccording to the fourth embodiment of the present invention;

FIG. 22 is a flowchart showing the processing procedure of thepathologist terminal according to the fourth embodiment of the presentinvention; and

FIG. 23 is a sequence chart showing the operation sequence of aninformation processing system including an information processingapparatus according to the fifth embodiment of the present invention.

DESCRIPTION OF EMBODIMENTS

Preferred embodiments of the present invention will now be described indetail with reference to the drawings. It should be noted that therelative arrangement of the components, the numerical expressions andnumerical values set forth in these embodiments do not limit the scopeof the present invention unless it is specifically stated otherwise.

[First Embodiment]

An information processing apparatus 100 according to the firstembodiment of the present invention will be described with reference toFIG. 1. The information processing apparatus 100 in FIG. 1 is anapparatus which assists diagnosis based on a tissue sample imageobtained by staining and capturing a tissue. As shown in FIG. 1, theinformation processing apparatus 100 includes a first receiver 101,first analyzer 102, determination unit 103, notification unit 104,second receiver 105, second analyzer 106, and transmitter 107.

The first receiver 101 receives lower-magnification image data 121 amonga plurality of image data obtained at different magnifications for anarea image 111 in a region selected in a tissue sample image 110. Thefirst analyzer 102 analyzes the area image 111 based on thelow-magnification image data 121 received by the first receiver 101, andgenerates first feature information. Based on the first featureinformation generated by the first analyzer 102, the determination unit103 determines whether analysis based on higher-magnification image datais necessary for the area image 111. When the determination unit 103determines that the analysis based on higher-magnification image data isnecessary, the notification unit 104 transmits a transmission request122 for higher-magnification image data of the area image 111. Thesecond receiver 105 receives high-magnification image data 123transmitted in response to the transmission request 122 from thenotification unit 104. The second analyzer 106 analyzes the area image111 based on the high-magnification image data 123 received by thesecond receiver 105, and generates second feature information. Thetransmitter 107 transmits the second feature information 124 generatedby the second analyzer 106.

According to the first embodiment, high-accuracy diagnosis assistancecan be quickly provided for a tissue sample image from a pathologistregardless of the restriction of the transmission capacity.

[Second Embodiment]

The second embodiment will describe a pathological image diagnosisassistance system in which a plurality of pathologist terminals and ananalysis center are connected via a network, and the analysis centeranalyzes a tissue sample image transmitted from the pathologist terminaland assists diagnosis. First, the pathologist terminal transmits alow-magnification area image of a selected region. Then, the analysiscenter analyzes the low-magnification area image, and determines whetherit is necessary to analyze a high-magnification area image. Ifnecessary, the analysis center requests the pathologist terminal totransmit a high-magnification area image. The analysis center analyzesthe high-magnification area image, and informs the pathologist terminalof the analysis result(s) which assists diagnosis. According to theembodiment, assistance of the analysis center for diagnosis by apathologist based on a tissue sample image can be quickly received athigh accuracy. Also, the diagnosis assistance service in the analysiscenter can be implemented with a small amount of resources.

<<Arrangement of Information Processing System>>

FIG. 2 is a chart showing the arrangement of a pathological imagediagnosis assistance system 200 serving as an information processingsystem according to the second embodiment.

The pathological image diagnosis assistance system 200 includes aninformation processing apparatus functioning as an analysis center 210,information processing apparatuses functioning as a plurality ofpathologist terminals 220, and a network 230 which connects the analysiscenter 210 and the pathologist terminals 220.

The analysis center 210 includes a communication controller 215 forcommunicating with the plurality of pathologist terminals 220 via thenetwork 230. The analysis center 210 also includes a low-magnificationimage analyzer 211 which analyzes a low-magnification area image of oneregion of interest (to be referred to as an ROI hereinafter) transmittedfrom the pathologist terminal 220, and if necessary as a result of theanalysis, requests transmission of a high-magnification area image ofthe same ROI. The low-magnification image analyzer 211 includes alow-magnification image table 212 used for analysis of alow-magnification area image and a high-magnification area imagetransmission request. Further, the analysis center 210 includes ahigh-magnification image analyzer 213 which analyzes ahigh-magnification area image of the same ROI transmitted from thepathologist terminal 220 and sends back the analysis result as diagnosisassistance information to the pathologist terminal 220. Thehigh-magnification image analyzer 213 includes a high-magnificationimage table 214 used for analysis of a high-magnification area image andtransmission of diagnosis assistance information.

Each pathologist terminal 220 includes a controller 221 which controlsthe operation of the pathologist terminal 220 and communication with theanalysis center 210. The pathologist terminal 220 also includes ascanner 222 which reads, at a resolution corresponding to a highmagnification, a pathological slide obtained by capturing a stainedtissue. Further, the pathologist terminal 220 includes a display 223which displays a tissue sample image read by the scanner 222. Assumethat necessary input/output devices are connected though FIG. 2 does notillustrate a keyboard, pointing device, or the like for data input andoperation instruction.

In the embodiment, the low magnification is “×10”, and the highmagnification is “×40”. When the magnification is expressed by theresolution of a tissue sample image, the low magnification is expressedas 3,000×3,000 pixels, and the high magnification is expressed as12,000×12,000 pixels. At the low magnification, a tissue structureincluding the shape of a duct and the like can be analyzed, but eachcell or cell nucleus cannot be analyzed. To the contrary, at the highmagnification, even each cell and cell nucleus can be analyzedaccurately.

<<Operation Sequence of Information Processing System>>

FIG. 3 is a sequence chart showing an operation sequence 300 of thepathological image diagnosis assistance system 200 serving as theinformation processing system according to the embodiment. In FIG. 3, anoperation from reading of a pathological slide by the scanner 222 of thepathologist terminal 220 up to screen display of diagnosis assistanceinformation will be explained.

First, in step S301, the pathologist terminal 220 reads tissue sampleimages from a pathological slide by using the scanner 222. Theembodiment assumes that the resolution of the scanner 222 corresponds tohigh-magnification image data of a tissue sample image, but theresolution does not have an upper limit. Then, in step S303, the display223 displays the read tissue sample images. A tissue area used fordiagnosis is selected from a plurality of tissue areas in the tissuesample image. Further, ROIs, analysis of which is requested of theanalysis center 210 for diagnosis assistance, are selected from theselected tissue area (see FIG. 4). Note that selection of a tissue areaand selection of ROIs may be designated by a pathologist from the tissuesample image on the screen of the display 223, or may be decided byexisting automatic ROI-setting software. For example, the automaticROI-setting software is a light software module which decides targetregions by calculation (for example, detection of regions deeply stainedin hematoxylin) of a small load, in comparison to a calculation amountfor a full-fledged cancer diagnosis which is performed in the analysiscenter 210. In this specification, this software module will be calledLWA (Light-Weight Analyzer).

In step S305, the pathologist terminal 220 transmits low-magnificationimage data of the selected ROIs to the analysis center 210. In theembodiment, a tissue sample image read by the scanner 222 corresponds tohigh-magnification image data. Hence, the low-magnification image datais generated by decreasing the resolution by thinning processing or thelike. At least the terminal ID of the pathologist terminal 220, an imagenumber for identifying an image, the portion (for example, stomach,lung, breast, or prostate) of the captured tissue, and the stainingmethod (for example, HE method, IHC method, or FISH method) are added tothe low-magnification image data to be transmitted, for the purpose ofanalysis and result transmission by the analysis center 210. The imagenumber is a number independent of personal information of a patient, andis assigned after conversion so that management of the personalinformation is completed within the pathologist terminal 220. Theassignment method will be explained with reference to FIG. 7A. Theportion of the tissue and the staining method are associated with eachother. If only either information suffices for selection of an analysismethod, only this information is used. For example, information of thesex and age, information of the address and nationality, and the likemay be added for analysis, or accumulation and analysis of informationin a database (to be referred to as a DB hereinafter) as long aspersonal information of the patient does not leak. Although a pluralityof ROIs are generally selected in one tissue area, low-magnificationimage data may be transmitted at once for a plurality of ROIs orindividually for the respective ROIs.

Upon receiving the low-magnification image data, in step S307, theanalysis center 210 performs simple tissue structure analysis by using atissue structure analysis DB which has been registered in advance bymachine learning based on low-magnification image data of ROIs. As aresult of the tissue structure analysis, in step S309, the analysiscenter 210 determines whether analysis using high-magnification imagedata is necessary because this ROI is considered to be a cancer cellcandidate. Note that the result of the tissue structure analysis and theresult of determining whether analysis using high-magnification imagedata is necessary sometime change depending on the portion of a tissueand the staining method. FIG. 8A exemplifies the tissue structureanalysis DB.

If analysis using high-magnification image data is unnecessary,determination is made for the next ROI. In the embodiment, the areaimage of each ROI is independently analyzed without associating it withthe patient and the transmission source pathologist terminal. Also, thearea image of each ROI may be analyzed independently of the area imageof another ROI in a tissue sample image obtained from the samepathological slide.

If the analysis center 210 determines that analysis usinghigh-magnification image data is necessary, it requests the pathologistterminal 220 to transmit high-magnification image data in step S311. Thetransmission request can identify an area image by the transmissionsource terminal ID and image number without transmitting patientinformation. The pathologist terminal 220 confirms, from thetransmission source terminal ID, that the request partner is thepathologist terminal 220 itself, and specifies high-magnification imagedata to be transmitted based on the image number. In step S313, todisplay the analysis results, the pathologist terminal 220 holds ROIinformation for which high-magnification image data has been requested.In step S315, the pathologist terminal 220 transmits the requestedhigh-magnification image data of the ROI to the analysis center 210together with the transmission source terminal ID and image number.

Upon receiving the high-magnification image data, in step S317, theanalysis center 210 performs fine feature analysis by using a featureanalysis DB which has been registered in advance by machine learningbased on high-magnification image data of ROIs. Note that the featureanalysis sometimes changes depending on the portion of a tissue and thestaining method. FIG. 8B exemplifies the feature analysis DB. In stepS319, the analysis center 210 transmits, as the analysis results to thepathologist terminal 220, feature analyzed based on thehigh-magnification image data, or feature information representing thefeature.

Upon receiving the analysis results, the pathologist terminal 220superimposes in step S321 the analysis results on the tissue sampleimage read from the pathological slide in step S301, and displays it onthe display 223 in step S323 (see FIGS. 5A and 5B). The pathologistdiagnoses the tissue sample image by referring to the analysis resultsdisplayed on the display 223 as assistance information.

Note that prediction of diagnosis from feature in the feature analysisof step S317 has already been implemented. In this case, a predicteddiagnosis may also be displayed on the display 223 to assist diagnosisin step S323.

<<Display Screen on Pathologist Terminal>>

A display screen on the display 223 in processing according to theembodiment will be explained with reference to FIGS. 4, 5A, and 5B.

(Display Screen in Area Image Transmission)

FIG. 4 is a view showing a screen 400 displayed on the display 223 ofthe pathologist terminal 220 when transmitting the area image of aselected ROI to the analysis center 210.

The screen 400 displays a plurality of selected ROIs 401 to 404 whichare superimposed on a tissue area selected from a tissue sample image.Low-magnification area images in the ROIs 401 to 404 are transmitted tothe analysis center 210 in order to obtain diagnosis assistanceinformation. The area images of the ROIs 401 to 404 may be transmittedat once or sequentially for each ROI. Note that the ROI is rectangularin FIG. 4, but may have another shape such as a circle or ellipse or ashape conforming to the contour of a cluster of cells.

In FIG. 4, information 405 includes management information of thedisplayed tissue sample image in the pathologist terminal 220, andinformation for identifying the analysis center 210 as a requestdestination of which diagnosis assistance is requested. Of these piecesof information, personal information such as the name is not transmittedto the analysis center 210. Note that the information 405 is merely anexample, and is not limited to this.

(Analysis Result Display Screen)

FIG. 5A is a view showing a first screen 510 obtained by displaying, onthe display 223 of the pathologist terminal 220, the result of analysisbased on the low-magnification image data in the analysis center 210.

In FIG. 5A, the analysis results of the ROIs 401 to 404 in FIG. 4 arerepresented by the difference of the line of a rectangular framesurrounding each ROI. A thin solid line indicates that an ROI 511 is acancer cell-free area for which no high-magnification image data need beanalyzed. Thick solid lines indicate that ROIs 512 and 513 requireanalysis of high-magnification image data and are areas where cancercells are obvious. A thick broken line indicates that an ROI 514requires analysis of high-magnification image data but is a cancercell-free area. Note that the analysis results are represented by thedifference of the line of a rectangular frame in FIG. 5A, but anotheridentifiable display such as the difference of the color is usable.

In FIG. 5A, information 515 includes management information of thedisplayed tissue sample image in the pathologist terminal 220, andinformation for identifying the analysis center 210 as a report sourcewhich has reported the analysis results for diagnosis assistance. Ofthese pieces of information, personal information such as the name ismanaged in the pathologist terminal 220. Note that the information 515is merely an example, and is not limited to this.

FIG. 5B is a view showing a second screen 520 obtained by displaying, onthe display 223 of the pathologist terminal 220, the result of analysisbased on the high-magnification image data in the analysis center 210.

In FIG. 5B, the analysis results of the ROIs 401 to 404 in FIG. 4 arerepresented by display of features analyzed in correspondence withrespective ROIs having cancer cells. No display of feature for ROIs 521and 524 means that the ROIs 521 and 524 are cancer cell-free areas. ROIs522 and 523 are displayed together with the values of the averagenucleus size (μm²), the average nuclear grade, and the texture asfeatures. Note that the feature changes depending on the portion of atissue and the staining method, and an example of the feature is shownin FIG. 8B. The display of FIG. 5B may be displayed in combination withthat of FIG. 5A.

In FIG. 5B, information 525 includes management information of thedisplayed tissue sample image in the pathologist terminal 220, andinformation for identifying the analysis center 210 as a report sourcewhich has reported the analysis results for diagnosis assistance. Ofthese pieces of information, personal information such as the name ismanaged in the pathologist terminal 220. Note that the information 525is merely an example, and is not limited to this.

<<Hardware Arrangement of Analysis Center>>

FIG. 6 is a chart showing the hardware arrangement of the analysiscenter 210 according to the embodiment. FIG. 6 shows the arrangement ofthe analysis center 210 formed from one apparatus, but the analysiscenter 210 may be formed from a plurality of apparatuses for respectivefunctions.

In FIG. 6, a CPU 610 is an arithmetic control processor, and implementsthe controller of the analysis center 210 by executing a program. A ROM620 stores permanent data and programs such as initial data andprograms. The communication controller 215 controls communication withthe plurality of pathologist terminals 220 via the network 230. Thiscommunication is arbitrarily wired or wireless.

A RAM 640 is a random access memory used as a temporary storage workarea by the CPU 610. In the RAM 640, areas for storing data necessary toimplement the embodiment are ensured. Each area stores reception data641 including image data of an area image received from the pathologistterminal 220. The RAM 640 stores the low-magnification image table 212for managing low-magnification image data received from the pathologistterminal 220 (see FIG. 7A). Also, the RAM 640 stores thehigh-magnification image table 214 for managing high-magnification imagedata received from the pathologist terminal 220 (see FIG. 7B). Further,the RAM 640 stores transmission data 642 which is to be transmitted tothe pathologist terminal 220 and includes analysis results.

A storage 650 is a large-capacity storage device which stores databases,various parameters, and programs to be executed by the CPU 610 in anonvolatile way. The storage 650 stores the following data or programsnecessary to implement the embodiment. As a data storage, the storage650 stores a tissue structure analysis DB 651 (see FIG. 8A) used toperform tissue structure analysis of an ROI based on low-magnificationimage data and determine whether analysis of high-magnification imagedata is necessary. Also, the storage 650 stores a feature analysis DB652 (see FIG. 8B) used to perform feature analysis of an ROI based onhigh-magnification image data. Note that the tissue structure analysisDB 651 and feature analysis DB 652 are desirably updated by feedback ofimage data and analysis results received from the pathologist terminal220, and learning using statistical processing of analysis results.

In the embodiment, as a program, the storage 650 stores a pathologicalimage diagnosis assistance program 653 which implements a series ofpathological image diagnosis assistances (see FIG. 9). The storage 650stores a tissue structure analysis module 654 which forms part of thepathological image diagnosis assistance program 653, and performs tissuestructure analysis of an ROI based on low-magnification image data byusing the tissue structure analysis DB 651. The storage 650 stores afeature analysis module 655 which forms part of the pathological imagediagnosis assistance program 653, and performs feature analysis of anROI based on high-magnification image data by using the feature analysisDB 652. The storage 650 stores an analysis result transmission module656 which transmits analysis results as diagnosis assistance informationto the pathologist terminal 220.

FIG. 6 shows only data and programs necessary for the embodiment, anddoes not show general-purpose data and programs such as an OS.

(Low-Magnification Image Table)

FIG. 7A is a chart showing the structure of the low-magnification imagetable 212 in FIGS. 2 and 6 for managing low-magnification image data.The low-magnification image table 212 allows specifying a transmissionsource transmitted low-magnification image data, and ROIs withoutpersonal information, so it is possible to request transmission ofhigh-magnification image data of the same ROIs. All the associationsbetween low-magnification image data and personal information are heldin the pathologist terminal 220 and do not leak outside.

The low-magnification image table 212 stores a terminal ID 701 of thepathologist terminal 220 as transmission source identifying informationof a transmission source which has transmitted low-magnification imagedata of an ROI. The low-magnification image table 212 stores, as areceived image number 702, an image number serving as image dataidentifying information assigned by the pathologist terminal 220. In theembodiment, low-magnification image data may be represented by “0” atthe most significant bit of the image number 702, but may be representedby another method. The terminal ID 701 and received image number 702 canidentify a pathologist terminal 220 and an ROI corresponding to imagedata in a high-magnification image data transmission request withoutpersonal information. Therefore, one number may be used as the terminalID 701 and received image number 702 for management of image data. Thelow-magnification image table 212 also stores received low-magnificationimage data 703. In the low-magnification image data 703, a pointerpointing a storage address of image data stored at another position maybe stored.

A portion 704 of a tissue, a staining method 705, and a sex/age 706 arepieces of information for selecting a tissue structure analysis methodfor the low-magnification image data 703. If the portion or stainingmethod is fixed to one type, these pieces of information areunnecessary. The sex/age 706, and another information for increasing theaccuracy of tissue structure analysis may be transmitted from thepathologist terminal 220 in response to a tissue structure analysisaccuracy request and referred to, or may not be transmitted. Inaddition, the low-magnification image table 212 stores a tissuestructure analysis result 707 for the low-magnification image data 703,and necessity 708 of a high-magnification image of the same ROI based onthe tissue structure analysis result.

(High-Magnification Image Table)

FIG. 7B is a chart showing the structure of the high-magnification imagetable 214 in FIGS. 2 and 6 for managing high-magnification image data.The high-magnification image table 214 allows specifying a transmissionsource transmitted high-magnification image data, and ROIs withoutpersonal information, and transmitting and managing an analysis result.All the associations between high-magnification image data and personalinformation are held in the pathologist terminal 220 and do not leakoutside.

The high-magnification image table 214 stores a terminal ID 711 of thepathologist terminal 220 serving as a transmission source which hastransmitted high-magnification image data of an ROI. Thehigh-magnification image table 214 stores, as a received image number712, an image number assigned by the pathologist terminal 220. In thiscase, “1” at the most significant bit of the image number 712 representshigh-magnification image data, and if the ROI is the same, a numberindicated by lower bits is managed as the same number as the imagenumber 702 in FIG. 7A. The terminal ID 711 and received image number 712can identify a pathologist terminal 220 and an ROI corresponding toimage data in an analysis result based on high-magnification image datawithout personal information. The high-magnification image table 214also stores received high-magnification image data 713. In thehigh-magnification image data 713, a pointer pointing a storage addressof image data stored at another position may be stored.

A portion 714 of a tissue, a staining method 715, and a sex/age 716 arepieces of information for selecting a feature analysis method for thehigh-magnification image data 713. If the portion or staining method isfixed to one type, these pieces of information are unnecessary. Thesex/age 716, and another information for increasing the accuracy offeature analysis may be transmitted from the pathologist terminal 220 inresponse to a feature analysis accuracy request and referred to, or maynot be transmitted. In addition, the high-magnification image table 214stores a feature analysis result 717 for the high-magnification imagedata 713, and analysis result informing data 718 generated for referenceby a pathologist based on the feature analysis result. Note that theanalysis result informing data is the feature analysis result itself,the analysis result informing data 718 need not be set separately.

(Tissue Structure Analysis DB)

FIG. 8A is a view showing the structure of the tissue structure analysisDB 651 in FIG. 6. Note that parameters used in tissue structureanalysis, calculation of respective features using them, and the likeare not a characteristic part of the embodiment and have already beenknown, so a description thereof will be omitted (see Japanese PatentLaid-Open No. 2006-153742).

Parameters 800 are used for the known tissue structure analysis.Determination conditions 801 to 805 have been registered in advance bymachine learning in order to determine the high-magnification imagenecessity 708 based on the tissue structure analysis result 707 in FIG.7A. The determination conditions change depending on the characteristicsof a tissue sample image itself such as the portion 801 of a tissue tobe analyzed, the staining method 802, and the others 803. Thehigh-magnification image data necessary condition 804 based on thetissue structure analysis result includes high-magnification image datanecessary condition parameters obtained in advance from a tissuestructure analysis result. These values may be thresholds or defineranges. These determination conditions are conditions to determinewhether the ROI is suspected of having cancer. If even one condition issatisfied, it may be determined that the ROI has a cancer, or if aplurality of conditions are satisfied, it may be determined that the ROIhas a cancer.

Features analyzed in tissue structure analysis using the HE stainingmethod will be simply explained, but the features are not limited to thefollowing description. As feature, special feature is sometimes useddepending on a target organ. However, the following features areimportant features for cancer at almost all portions. f1 to f10 shown inFIG. 8A are as follows:

-   f1) nucleus size-   f2) density of large nuclei=number of large nuclei/total number of    nuclei-   f3) density of nuclei belonging to a duct-   f4) nucleus orientation-   f5) nucleus flatness-   f6) duct thickness-   f7) color (RGB)-   f8) color (HSV)-   f9) duct region-   f10) signal (orientation feature and alignment) filtered by the    Gabor function

As a global feature, information about the region such as the mucus orfat is sometimes used in addition to the above features. As specialfeature, for example, there is a suspected signet ring cell (to bereferred to as a signet ring hereinafter) in gastric biopsy.

In actual condition judgment, the above features are used as basicfeatures, and derivatively obtained statistical amounts such as theaverage, variance, median, quartile, and histogram P-percentile (forexample, P=5, 25, 50, 75, 95) are calculated for each ROI and used asthe features of the ROI.

If one or a combination of these conditions satisfies a condition thatthe ROI has a cancer, “necessary” is copied from the high-magnificationimage necessity 805 to the high-magnification image necessity 708 in thelow-magnification image table 212 to request high-magnification imagedata and perform more detailed feature analysis.

(Feature Analysis DB)

FIG. 8B is a view showing the structure of the feature analysis DB 652in FIG. 6. Note that parameters used in feature analysis, calculation ofrespective features using them, and the like are not a characteristicpart of the embodiment and have already been known, so a descriptionthereof will be omitted (see Japanese Patent Laid-Open No. 2006-153742).

Parameters 810 are used in the known feature analysis. Determinationcriteria 811 to 815 have been registered in advance by machine learningin order to generate the analysis result informing data 718 from thefeature analysis result 717 in FIG. 7B. The determination criteriachange depending on the characteristics of a tissue sample image itselfsuch as the portion 811 of a tissue to be analyzed, the staining method812, and the others 813. The cancer cell presence/absence determinationcriterion 814 includes cancer cell presence/absence determinationcondition parameters obtained in advance from a feature analysis result.These values may be thresholds or define ranges. These judgmentconditions are conditions to judge whether the ROI has been suspected ofhaving a cancer and is concluded to have a cancer, or the ROI has beensuspected of having a cancer and is concluded to have a benign disease.If even one condition is satisfied, it may be judged that the ROI has acancer, or if a plurality of conditions are satisfied, it may be judgedthat the ROI has a cancer.

Features analyzed in feature analysis using the HE staining method willbe simply explained, but the features are not limited to the followingdescription. As feature, special feature is sometimes used depending ona target organ. However, the following features are important featuresfor cancer at almost all portions. F1 to F7 shown in FIG. 8B are asfollows:

-   F1) nucleus size-   F2) major and minor axes of a nucleus-   F3) circularity (it takes a maximum value of 1 as the shape is    almost a circle, and a smaller value as the shape deviates from a    circle)-   F4) texture-   F5) color (RGB)-   F6) color (HSV)-   F7) duct region

As special feature, for example, the presence/absence of a signet ringis confirmed based on high-magnification image data when there is asuspected signet ring cell (to be referred to as a signet ringhereinafter) in gastric biopsy based on low-magnification image data.

In actual condition judgment, the above features are used as basicfeatures, and derivatively obtained statistical amounts such as theaverage, variance, median, quartile, and histogram P-percentile (forexample, P=5, 25, 50, 75, 95) are calculated for each ROI and used asthe features of the ROI.

If one or a combination of these conditions satisfies a condition thatthe ROI has a cancer, the result is copied to the presence/absence 815of a cancer cell, and transmitted to the transmission source of thetissue sample image for diagnosis assistance.

Note that features of the same name used in analysis oflow-magnification image data and analysis of high-magnification imagedata are not equal because the resolutions of the images are different.For example, in analysis of low-magnification image data, the nuclearsize is roughly analyzed by extracting a region stained in hematoxylin,and classifying nuclei into large and small ones based on the pixelsize. To the contrary, in analysis of high-magnification image data, thecontour of a nucleus is accurately extracted to calculate the size (orcircularity or the like) based on the contour.

Global information such as a duct is obtained at only a lowmagnification. For this reason, first, a duct region is extracted togenerate a duct mask in analysis of low-magnification image data, andthe mask information is directly transferred to the high-magnificationimage data analysis module. Based on this information, thehigh-magnification image data analysis module checks whether the ductcontains a nucleus to be analyzed, and if the duct contains a nucleus tobe analyzed, determines that this nucleus is not a cancer even if itssize is large.

The analysis of low-magnification image data and the analysis ofhigh-magnification image data do not have a simple primaryanalysis-secondary analysis relationship, but a detailed descriptionthereof will be omitted in the embodiment for brevity.

<<Operation Procedure of Analysis Center>>

FIG. 9 is a flowchart showing the operation procedure of the analysiscenter 210. The CPU 610 in FIG. 6 executes this flowchart by using theRAM 640, thereby implementing the function of the analysis center 210 inFIG. 2.

First, in step S901, the analysis center 210 waits for reception of animage from the pathologist terminal 220. If the analysis center 210receives an image, the process advances to step S903, and the analysiscenter 210 stores and holds information including the terminal ID of thetransmission source of the received image data, the image number, theportion, the staining method, and the sex/age. In step S905, theanalysis center 210 stores and holds the transmitted image data. In theembodiment, which of low-magnification image data and high-magnificationimage data is the received image data is discriminated from the imagenumber, and the pieces of information stored and held in steps S903 andS905 are stored in the low-magnification image table 212 of FIG. 7A orthe high-magnification image table 214 of FIG. 7B.

In step S907, the process branches in correspondence with thediscrimination of which of low-magnification image data andhigh-magnification image data is the received image data. If thereceived image data is low-magnification image data, the processadvances to step S909, and the analysis center 210 performs tissuestructure analysis of the low-magnification image data corresponding tothe portion, staining method, sex/age, and the like. In the tissuestructure analysis performed here, for example, for an HE-stained tissueof a stomach, screening of cancer candidate regions is performed basedon disturbance of a duct shape using a known InfoMax algorithm, or thelike. In step S911, the analysis center 210 judges, from the result ofthe tissue structure analysis, whether analysis of high-magnificationimage data of the same ROI is necessary. If analysis of ahigh-magnification image of the same ROI is necessary, the processadvances to step 913, and the analysis center 210 requests thepathologist terminal 220 serving as the transmission source to transmithigh-magnification image data of the same ROI.

If the received image data is high-magnification image data, the processadvances to step S915, and the analysis center 210 performs featureanalysis of the high-magnification image data corresponding to theportion, staining method, sex/age, and the like. In the feature analysisperformed here, for example, for an HE-stained tissue of a stomach, thedimensions and shape of a cell nucleus are analyzed using a known SVMalgorithm. In step S917, the analysis center 210 transmits the featureanalysis result of the high-magnification image data to the pathologistterminal 220 serving as the transmission source together with the imagenumber obtained from the transmission source.

<<Hardware Arrangement of Pathologist Terminal>>

FIG. 10 is a chart showing the hardware arrangement of the pathologistterminal 220 according to the embodiment. As shown in FIG. 2, thepathologist terminal 220 includes the controller 221, scanner 222, anddisplay 223 as basic components.

In FIG. 10, a CPU 1010 is an arithmetic control processor, andimplements the controller of the pathologist terminal 220 by executing aprogram. A ROM 1020 stores permanent data and programs such as initialdata and programs. A communication controller 1030 controlscommunication with the analysis center 210 via the network 230. Thiscommunication is arbitrarily wired or wireless.

A RAM 1040 is a random access memory used as a temporary storage workarea by the CPU 1010. In the RAM 1040, areas for storing data necessaryto implement the embodiment are ensured. Each area stores tissue sampleimage read data 1041 read from a pathological slide by the scanner 222.The RAM 1040 stores an image identification table 1042 for managinglow-magnification image data and high-magnification image data to betransmitted to the analysis center 210, and specifying a patient,portion, ROI, and the like (see FIG. 11). Also, the RAM 1040 storestransmission/reception data 1043 to be transmitted to and received fromthe analysis center 210 (see FIG. 11). Further, the RAM 1040 storesdisplay data 1044 to be displayed on the display 223 of the pathologistterminal 220.

A storage 1050 is a large-capacity storage device which storesdatabases, various parameters, and programs to be executed by the CPU1010 in a nonvolatile way. The storage 1050 stores the following data orprograms necessary to implement the embodiment. As a data storage, thestorage 1050 stores a tissue sample image DB 1051 which is obtained byreading by the scanner 222 and is locally accumulated by a pathologist.Also, the storage 1050 stores a patient history DB 1052 which holds adiagnosis history corresponding to a patient. In a system whichintensively accumulates and manages information necessary for theanalysis center 210, which will be described in the fourth embodiment,it is only necessary that the tissue sample image DB 1051 and patienthistory DB 1052 of the pathologist terminal 220 store parametersallowing access to information in the analysis center 210 from thepathologist terminal 220.

In the embodiment, as a program, the storage 1050 stores a pathologicalimage diagnosis processing program 1053 including processing to requestpathological image diagnosis assistance of the analysis center 210 (seeFIG. 12). The storage 1050 stores an ROI selection module 1054 whichforms part of the pathological image diagnosis processing program 1053,and selects a tissue area and ROIs to be diagnosed from a tissue sampleimage. The storage 1050 stores a transmission/reception control module1055 which forms part of the pathological image diagnosis processingprogram 1053, and controls data communication with the analysis center210. The storage 1050 stores an analysis result display module 1056which superimposes and displays, on a tissue sample image, analysisresults received from the analysis center 210.

An input interface 1060 is an interface which inputs control signals anddata necessary for control by the CPU 1010. In the embodiment, the inputinterface 1060 inputs image data of a tissue sample image obtained byreading a pathological slide by the scanner 222. Note that a keyboard,pointing device, and the like are not illustrated. An output interface1070 is an interface which outputs control signals and data to a deviceunder the control of the CPU 1010. In the embodiment, the outputinterface 1070 outputs a tissue sample image to the display 223,diagnosis assistance request information to the analysis center 210, oranalysis results transmitted from the analysis center 210.

FIG. 10 shows only data and programs necessary for the embodiment, anddoes not show general-purpose data and programs such as an OS.

(Image Identification Table and Transmission/reception Data)

FIG. 11 is a chart showing the structures of the image identificationtable 1042 and transmission/reception data 1043 shown in FIG. 10.

Reference numeral 1101 denotes a patient ID for identifying a patient;and 1102, a sex/age of a patient that is information for specifying apatient. Although another specifying information such as the address ofa patient is also stored, FIG. 11 shows only information necessary forprocessing in the embodiment. Reference numeral 1103 denotes a portionof a tissue sample image to be analyzed; and 1104, a staining method ofthe tissue that is information associated with an analysis method in theanalysis center 210.

Reference numeral 1105 denotes a slide ID for identifying a pathologicalslide; 1106, a tissue area ID for identifying a tissue area to beanalyzed in a tissue sample image read from a pathological slide by thescanner 222; and 1107, an ROL_ID for identifying an ROI to be analyzedin an area. In the embodiment, upper left and lower right positionaddresses of a rectangle representing the ROL_ID 1107 in a tissue sampleimage are stored in 1108. Note that position storage data changesdepending on the ROI shape.

In the embodiment, of the data 1101 to 1108, information associated withan analysis method in the analysis center 210 is transmitted to theanalysis center 210, but the remaining information about personalinformation of a patient is not transmitted to the analysis center 210.That is, a unique image number 1109 which is not associated withpersonal information of a patient and is to be transmitted from thepathologist terminal 220 is assigned to the image of an ROI specified bythe data 1101 to 1108.

Reference numeral 1110 denotes low-magnification image data of an ROIspecified by the image number 1109 to be transmitted; and 1111,high-magnification image data of the ROI specified by the image number1109 to be transmitted. Further, an analysis result 1112 reported fromthe analysis center 210, and a result 1113 of a diagnosis made by apathologist by referring to the analysis result 1112 as assistanceinformation are stored.

As is apparent from FIGS. 11, 7, and 8, communication of the image dataand analysis result of an ROI between the pathologist terminal 220 andthe analysis center 210 is basically performed using image informationwhich is not associated with personal information of a patient and isassigned by the pathologist terminal 220.

<<Operation Procedure of Pathologist Terminal>>

FIG. 12 is a flowchart showing the operation procedure of thepathologist terminal 220 according to the embodiment. The CPU 1010 inFIG. 10 executes this flowchart by using the RAM 1040, therebyimplementing the function of the pathologist terminal 220 in FIG. 2.

First, the pathologist terminal 220 reads a pathological slide by thescanner 222 at a resolution corresponding to the high magnification instep S1201, and stores the read high-magnification image data in stepS1203. In step S1205, the pathologist terminal 220 displays a tissuesample image corresponding to the pathological slide on the display 223.In step S1207, a tissue area as an analysis request target is selectedfrom the tissue sample image corresponding to the pathological slide, anROI is selected from the tissue area, and an image number is assigned tothe image of the ROI. Note that the processing in step S1207 may beautomatically performed by the LWA installed in the pathologist terminal220, or may be performed by interaction with a pathologist using a touchpanel on the display screen. An example in which a plurality of ROIsselected to request analysis are superimposed and displayed on theselected tissue area as a result of the processing in step S1207corresponds to FIG. 4.

In step S1209, the pathologist terminal 220 generates low-magnificationimage data of the selected ROI. The low-magnification image datageneration method can be an existing method and, for example, thinningprocessing is easy. For conversion from the magnification (×40) into themagnification (×10) in the embodiment, three pixels are thinned out. Instep S1211, the pathologist terminal 220 transmits the generatedlow-magnification image data to the analysis center 210 together withthe assigned image number. Note that the terminal ID for identifying thepathologist terminal 220, and information associated with the analysismethod in the analysis center 210 are also transmitted together.

In step S1213, the pathologist terminal 220 judges whether the analysiscenter 210 requests transmission of high-magnification image data of thesame ROI. If there is a high-magnification image data transmissionrequest, the process advances to step S1215, and the pathologistterminal 220 stores and holds the requested ROI(s) for display of theanalysis result(s). In step S1217, the pathologist terminal 220transmits high-magnification image data of the requested ROI to theanalysis center 210 together with the image number. If there is nohigh-magnification image data transmission request, the process advancesto step S1219.

In step S1219, the pathologist terminal 220 waits for reception ofanalysis results from the analysis center 210. If the pathologistterminal 220 receives analysis results, the process advances to stepS1221, and the pathologist terminal 220 generates a display screen bysuperimposing the analysis results as numerical data (see FIG. 5B) onthe tissue area to be analyzed, or by generating display data in the ROIframe color corresponding to the analysis results (see FIG. 5A) andsuperimposing them on the tissue area to be analyzed. In step S1223, thepathologist terminal 220 displays the generated display screen on thedisplay 223 to assist diagnosis by the pathologist.

[Third Embodiment]

In the second embodiment, analysis targets in the analysis center 210are limited to ROIs in one tissue area selected from the tissue sampleimage of a pathological slide. In the third embodiment, feature analysisis performed by referring to even ROIs in another tissue area of thesame pathological slide. According to the third embodiment, even whenanalysis of only a selected tissue area is insufficient for diagnosis,assistance of the analysis center for diagnosis by a pathologist basedon a tissue sample image can be quickly received at high accuracy.

The arrangements of an information processing system, analysis center,and pathologist terminal according to the third embodiment are similarto those in the second embodiment and can be inferred, so a descriptionthereof will not be repeated.

<<Operation Sequence of Information Processing System>>

FIG. 13 is a sequence chart showing an operation sequence 300 of apathological image diagnosis assistance system 200 serving as theinformation processing system according to the embodiment. In FIG. 13,an operation from reading of a pathological slide by a scanner 222 of apathologist terminal 220 up to feature analysis by an analysis center210 will be explained. An operation (steps S319 to S323) after thefeature analysis is the same as that in FIG. 3 of the second embodiment,and a description thereof will not be repeated.

Processes in steps S301 to S315 are the same as those in FIG. 3 of thesecond embodiment. Whether high-magnification image data is necessary isdetermined by tissue structure analysis of low-magnification image dataof an ROI. If necessary, the analysis center 210 requests thepathologist terminal 220 to transmit high-magnification image data.

In the third embodiment, if no high-magnification image data isnecessary (NO in step S309) or after high-magnification image data istransmitted (step S315), it is determined in step S1309 whether analysisof an image of another tissue area is necessary for diagnosisassistance.

For example, it is generally designed to, when a region suspected tohave a cancer is detected in analysis of low-magnification image data(10X), select eight region images from the region of thelow-magnification image and analyze them based on high-magnificationimage data. If the number of regions suspected to have a cancer in thetissue area is smaller than eight, a region suspected to have a cancerneeds to be further selected from another tissue area. Note that “eightregions” are empirically decided, and the number of regions is notalways limited to eight. Since analysis of high-magnification image datatakes time, the number of regions is decided by taking account of thetradeoff between accuracy and time. The criterion to select eightregions is, for example, the nuclear density of a region determined tohave a cancer in analysis of low-magnification image data, and regionshaving higher densities are preferentially selected.

When an image of another tissue area is analyzed remotely, transmittingall eight regions via the network is inefficient, so the followingprocessing is desirably performed. First, high-magnification image dataof one region is transmitted and analyzed. If the presence of a canceris determined, transmission of high-magnification image data ends, thefinal determination is a cancer, and the processing ends. If the absenceof a cancer is determined in analysis of the high-magnification imagedata, the next high-magnification image data is requested to continuethe analysis. If cancer is denied in analysis of all the eighthigh-magnification image data, the final determination is “benign”.According to this data transfer method, the processing ends when thepresence of a cancer is determined. Thus, all eight high-magnificationimage data need not be transmitted, the data transfer amount isdecreased, and the total diagnosis time is shortened. The same effect isobtained even when eight regions exist in an initially requested tissuearea.

If analysis of a selected tissue area is sufficient without analyzinganother tissue area, the process advances to step S317 to performfeature analysis based on high-magnification image data. If analysis ofanother tissue area is necessary, the analysis center 210 requests, ofthe pathologist terminal 220, an image of another tissue area in stepS1311. In step S1313, the pathologist terminal 220 selects anothertissue area in accordance with the request, holds the selectedinformation, and selects an ROI from the tissue area. In step S1315, thepathologist terminal 220 transmits high-magnification image data of theROI of the selected tissue area to the analysis center 210.

In the feature analysis of step S317, the analysis center 210 performsanalysis of image data of ROIs of another tissue area requiringadditional analysis, in addition to analysis of image data of ROIsselected first. This analysis results are also displayed as diagnosisassistance information on the pathologist terminal 220. FIG. 13 shows,as separate processes, request and transmission of high-magnificationimage data of ROIs selected first, and those of high-magnification imagedata of ROIs of another tissue area requiring additional analysis.However, these high-magnification image data may be requested andtransmitted simultaneously.

[Fourth Embodiment]

In the second and third embodiments, the analysis center 210 onlyinforms the pathologist terminal 220 of, as diagnosis assistanceinformation, the analysis results of image data of ROIs sent from thepathologist terminal 220. In the fourth embodiment, an analysis center1410 accumulates, as a case DB, image data of ROIs which have beenanalyzed so far for diagnosis assistance, and diagnosis results made bypathologists who referred to the analysis results. When informing apathologist terminal 220 of an analysis results, the analysis center1410 further informs it of reference data based on the case DB.According to the embodiment, assistance of the analysis center fordiagnosis by a pathologist based on a tissue sample image can be quicklyreceived at high accuracy in consideration of not only judgment by onepathologist but also the learning results of the relationships betweentissue sample images, analysis results, and diagnosis results by manypathologists. Further, the pathologist terminal 220 can always refer todiagnosis cases, reducing the necessity to manage past diagnosis casesin the pathologist terminal 220.

<<Arrangement of Information Processing System>>

FIG. 14 is a chart showing the arrangement of a pathological imagediagnosis assistance system 1400 serving as an information processingsystem according to the embodiment. Note that the same referencenumerals denote building components having the same functions as thosein FIG. 2. A difference of FIG. 14 from FIG. 2 is only the arrangementof an analysis center 1410, and the same reference numerals denote thesame functional components.

The pathological image diagnosis assistance system 1400 includes aninformation processing apparatus functioning as the analysis center1410, information processing apparatuses functioning as a plurality ofpathologist terminals 220, and a network 230 which connects the analysiscenter 1410 and the pathologist terminals 220.

The analysis center 1410 includes a communication controller 1415 forcommunicating with the plurality of pathologist terminals 220 via thenetwork 230. The analysis center 1410 also includes a low-magnificationimage analyzer 211 which analyzes a low-magnification area image of oneROI transmitted from the pathologist terminal 220, and if necessary as aresult of the analysis, requests transmission of a high-magnificationarea image of the same ROI. The low-magnification image analyzer 211includes a low-magnification image table 212 used for analysis of alow-magnification area image and a high-magnification area imagetransmission request. Further, the analysis center 1410 includes ahigh-magnification image analyzer 1413. The high-magnification imageanalyzer 1413 analyzes a high-magnification area image of the same ROItransmitted from the pathologist terminal 220, and sends back theanalysis result as diagnosis assistance information to the pathologistterminal 220. Together with the analysis result, the high-magnificationimage analyzer 1413 sends back, to the pathologist terminal 220,auxiliary diagnosis information obtained by referring to past areaimages, analysis results, and diagnosis results accumulated in adiagnosis case DB 1416. The high-magnification image analyzer 1413includes a high-magnification image table 214 used for analysis of ahigh-magnification area image and transmission of diagnosis assistanceinformation. The diagnosis case DB 1416 accumulates the tissue sampleimages, analysis results, and diagnosis results of ROIs in associationwith each other based on the notifications of diagnosis results obtainedby referring to analysis results from the respective pathologistterminals 220. The diagnosis case DB 1416 is looked up to generateauxiliary diagnosis information.

Note that the arrangement of the pathologist terminal 220 is the same asthat in the second embodiment, and a description thereof will not berepeated.

<<Operation Sequence of Information Processing System>>

FIG. 15 is a sequence chart showing an operation sequence 1500 of thepathological image diagnosis assistance system 1400 serving as theinformation processing system according to the embodiment. In FIG. 15,the sequence in FIG. 3 (or FIG. 13) is executed before the start or atan omitted part at the center. However, FIG. 15 does not show this, andshows only a characteristic part of the embodiment. That is, FIG. 15shows an operation from input of a diagnosis result by a pathologist inthe pathologist terminal 220 to accumulation of a case in the diagnosiscase DB 1416. The omitted part at the center is accompanied by theoperation in FIG. 3 from transmission of requested high-magnificationimage data of an ROI(s) to display of an analysis result(s) anddiagnosis assistance information in the pathologist terminal 220.

After display of analysis results (step S323) in FIG. 3 or 13, in stepS1501, the pathologist terminal 220 waits for input of a diagnosisresult made by a pathologist by referring to the analysis results. Ifthe diagnosis result is input, the process advances to step S1503, andthe pathologist terminal 220 transmits the input diagnosis result and acorresponding treatment method to the analysis center 1410 (see FIG.16). The pathologist terminal 220 transmits the diagnosis result andtreatment method together with a terminal ID, and image numbers foridentifying images of the same ROIs as that in FIG. 3 (step S1505).

Upon receiving the diagnosis result and treatment method, in step S1507,the analysis center 1410 reassigns image numbers in the analysis center1410 in order to accumulate the image of the ROIs in the analysis center1410 as data independent of the transmission source, patient, and thelike. In step S1509, the analysis center 1410 notifies the pathologistterminal 220 of the reassigned image numbers. In step S1511, thepathologist terminal 220 holds the notified reassigned image numbers inassociation with personal information such as the patient. With thissetting, the analysis center 1410 can accumulate and manage dataindependently of personal information, and if necessary, the pathologistterminal 220 can read out image data of ROIs, analysis of which has beenrequested by the pathologist terminal 220. In step S1513, the analysiscenter 1410 accumulates the received diagnosis results and treatmentmethod in the diagnosis case DB 1416 in association with the reassignedimage numbers, image data, and analysis results. Note that all pieces ofinformation need not be accumulated in the diagnosis case DB 1416, andinformation which will help future auxiliary diagnosis may be screenedand accumulated. However, when the analysis center 1410 is also used asan information accumulation server for the pathologist terminal 220, allpieces of information transmitted from the pathologist terminal 220 fordiagnosis assistance are accumulated.

At the omitted part, the processes in steps S301 to S313 of FIG. 3 areperformed in response to a new diagnosis assistance request from thepathologist terminal 220. High-magnification image data is transmittedto the analysis center 1410 in step S315, feature analysis is performedin step S317, and then auxiliary diagnosis information is generated bylooking up the diagnosis case DB 1416 in step S1519. In step S1521, theauxiliary diagnosis information is reported to the pathologist terminal220 together with the diagnosis result. In step S1523, the pathologistterminal 220 generates a display image by superimposing the receiveddiagnosis result and auxiliary diagnosis information on a tissue areaselected from a tissue sample image. In step S1525, the display image isdisplayed on a display 223 to assist diagnosis by the pathologist (seeFIG. 17).

<<Display Screen in Pathologist Terminal>>

A display screen on the display 223 in processing according to theembodiment will be explained with reference to FIGS. 16 and 17.

(Display Screen in Diagnosis Result Transmission)

FIG. 16 is a view showing a screen 1600 displayed on the display 223 ofthe pathologist terminal 220 when transmitting a diagnosis result andtreatment method for a selected ROI to the analysis center 1410.

The screen 1600 displays a plurality of selected ROIs 1601 to 1604 whichare superimposed on a tissue area selected from a tissue sample image.Of the ROIs 1601 to 1604, the ROI 1601 is deleted from the ROIs by apathologist. The ROI 1604 is changed from “malignant” to “benign” by thepathologist. The ROIs 1602 and 1603 remain unchanged from analysisresults.

In FIG. 16, information 1605 includes management information of thedisplayed tissue sample image in the pathologist terminal 220,information for identifying the analysis center 1410 as a reportdestination to which a diagnosis result is reported, and a diagnosisresult and treatment method by the pathologist. Of these pieces ofinformation, personal information such as the name is not transmitted tothe analysis center 1410. Note that the information 1605 is merely anexample, and is not limited to this.

(Display Screen of Analysis Result and Auxiliary Diagnosis Information)

FIG. 17 is a view showing a screen 1700 obtained by displaying, on thedisplay 223 of the pathologist terminal 220, the result of analysis inthe analysis center 1410 and auxiliary diagnosis information.

In FIG. 17, the analysis results of a plurality of ROIs 1701 to 1704 arerepresented by the difference of the line of a rectangular framesurrounding each ROI. A thin solid line indicates that the ROI 1701 is acancer cell-free area for which no high-magnification image data need beanalyzed. Thick solid lines indicate that the ROIs 1702 and 1703 requireanalysis of high-magnification image data and are areas where cancercells are obvious. A thick broken line indicates that the ROI 1704requires analysis of high-magnification image data but is a cancercell-free area. Note that the analysis results are represented by thedifference of the line of a rectangular frame in FIG. 17, but anotheridentifiable display such as the difference of the color is usable.

In FIG. 17, information 1705 includes management information of thedisplayed tissue sample image in the pathologist terminal 220, andinformation for identifying the analysis center 1410 as a report sourcewhich has reported the analysis results for diagnosis assistance. Ofthese pieces of information, personal information such as the name ismanaged in the pathologist terminal 220. In addition, in FIG. 17,auxiliary diagnosis information 1706 including at least one ofinformation representing whether the condition of a disease is malignantor benign, prediction of a future medical record, and auxiliaryinformation of a treatment plan is displayed. The auxiliary diagnosisinformation 1706 is generated by looking up the diagnosis case DB 1416based on the analysis results of image data of ROIs in the analysiscenter 1410. Note that the pieces of information 1705 and 1706 aremerely examples, and are not limited to them. For example, as theauxiliary diagnosis information 1706, information representing whetherthe symptom is malignant or benign, the average survival time, and thetreatment plan are displayed. However, the auxiliary diagnosisinformation 1706 may include the presence/absence of metastasis, therecurrence rate, and the like.

<<Hardware Arrangement of Analysis Center>>

FIG. 18 is a chart showing the hardware arrangement of the analysiscenter 1410 according to the embodiment. FIG. 18 shows the arrangementof the analysis center 1410 formed from one apparatus, but the analysiscenter 1410 may be formed from a plurality of apparatuses for respectivefunctions. In FIG. 18, the same reference numerals as those in FIG. 6denote the same functional components.

In FIG. 18, a CPU 610 is an arithmetic control processor, and implementsthe controller of the analysis center 1410 by executing a program. A ROM620 stores permanent data and programs such as initial data andprograms. The communication controller 1415 controls communication withthe plurality of pathologist terminals 220 via the network 230. Thecommunication controller 1415 receives a diagnosis result and treatmentmethod from the pathologist terminal 220, and transfers them to thediagnosis case DB 1416. This communication is arbitrarily wired orwireless.

A RAM 1840 is a random access memory used as a temporary storage area bythe CPU 610. In the RAM 1840, areas for storing data necessary toimplement the embodiment are ensured. Each area stores reception data1841 including image data of an area image received from the pathologistterminal 220. The reception data 1841 includes a diagnosis result andtreatment method in addition to image data of an ROI. The RAM 1840stores the low-magnification image table 212 for managinglow-magnification image data received from the pathologist terminal 220(see FIG. 7A). Also, the RAM 1840 stores the high-magnification imagetable 214 for managing high-magnification image data received from thepathologist terminal 220 (see FIG. 7B). Further, the RAM 1840 storestransmission data 1842 which is to be transmitted to the pathologistterminal 220, and includes analysis results. In addition to the analysisresults, the transmission data 1842 includes auxiliary diagnosisinformation.

A storage 1850 is a large-capacity storage device which storesdatabases, various parameters, and programs to be executed by the CPU610 in a nonvolatile way. The storage 1850 stores the following data orprograms necessary to implement the embodiment. As a data storage, thestorage 1850 stores a tissue structure analysis DB 651 used to performtissue structure analysis of an ROI based on low-magnification imagedata. Also, the storage 1850 stores a feature analysis DB 652 used toperform feature analysis of an ROI based on high-magnification imagedata. Further, the storage 1850 stores the diagnosis case DB 1416 whichaccumulates diagnosis results and treatment methods in association withimage data of ROIs (see FIG. 19).

In the embodiment, as a program, the storage 1850 stores a pathologicalimage diagnosis assistance program 1853 which implements a series ofpathological image diagnosis assistances (see FIG. 20). The storage 1850stores a tissue structure analysis module 654 which forms part of thepathological image diagnosis assistance program 1853, and performstissue structure analysis of an ROI based on low-magnification imagedata by using the tissue structure analysis DB 651. The storage 1850stores a feature analysis module 1855 which forms part of thepathological image diagnosis assistance program 1853, performs featureanalysis of an ROI based on high-magnification image data by using thefeature analysis DB 652, and generates auxiliary diagnosis informationby looking up the diagnosis case DB 1416. The storage 1850 stores ananalysis result transmission module 1856 which transmits analysisresults and auxiliary diagnosis information as diagnosis assistanceinformation to the pathologist terminal 220.

FIG. 18 shows only data and programs necessary for the embodiment, anddoes not show general-purpose data and programs such as an OS.

(Diagnosis Case DB)

FIG. 19 is a chart showing the structure of data accumulated in thediagnosis case DB 1416. The diagnosis case DB 1416 manages data by imagenumbers reassigned uniquely by the analysis center 1410. The data arecompletely independent of personal information such as the patient andtransmission source, and managed for respective ROIs. Only thepathologist terminal 220 serving as a transmission source is notified ofthe reassigned image number. Therefore, personal information does notleak outside, and the transmission source can always access the data.

The diagnosis case DB 1416 is managed by a reassigned image number 1901.High-magnification image data 1902 of an ROI is stored in correspondencewith each reassigned image number 1901. In the high-magnification imagedata 1902, a pointer pointing a storage address of image data stored atanother position may be stored. Also, the diagnosis case DB 1416 storeslink information 1903 and accumulation date & time 1904 of an image torepresent the transition of a symptom of the same patient. Further, thediagnosis case DB 1416 stores a portion 1905 of a tissue, a stainingmethod 1906, and a sex/age 1907 associated with an analysis method anddiagnosis method. The diagnosis case DB 1416 stores an analysis result1908 of image data in the analysis center 1410, and a diagnosis result1909 and treatment method 1910 determined by a pathologist at thetransmission source using the analysis results 1908 as assistanceinformation.

<<Operation Procedure of Analysis Center>>

FIG. 20 is a flowchart showing the operation procedure of the analysiscenter 1410. The CPU 610 in FIG. 18 executes this flowchart by using theRAM 640, thereby implementing the function of the analysis center 1410in FIG. 14. Note that the same reference numerals as those in FIG. 9denote the same steps.

First, in step S901, the analysis center 1410 waits for reception of animage from the pathologist terminal 220. If the analysis center 1410receives an image, the process advances to step S903, and the analysiscenter 1410 stores and holds information including the terminal ID ofthe transmission source of the received image data, the image number,the portion, the staining method, and the sex/age. In step S905, theanalysis center 1410 stores and holds the transmitted image data. In theembodiment, which of low-magnification image data and high-magnificationimage data is the received image data is discriminated from the imagenumber, and the pieces of information stored and held in steps S903 andS905 are stored in the low-magnification image table 212 of FIG. 7A orthe high-magnification image table 214 of FIG. 7B.

If no image is received (NO in step S901), the analysis center 1410judges in step S2001 whether the diagnosis result and treatment methodare received. If the diagnosis result and treatment method are received,the process advances to step S2003, and the analysis center 1410uniquely reassigns an image number. In step S2005, the analysis center1410 notifies only the pathologist terminal 220 serving as thetransmission source of the reassigned image number. In step S2007, theanalysis center 1410 adds the diagnosis result and treatment method tothe image data and analysis results of ROIs, and records them in thediagnosis case DB 1416.

In step S907, the process branches in correspondence with thediscrimination of which of low-magnification image data andhigh-magnification image data is the received image data. If thereceived image data is low-magnification image data, the processadvances to step S2009, and the analysis center 1410 performs tissuestructure analysis of the low-magnification image data corresponding tothe portion, staining method, sex/age, and the like. Note that thetissue structure analysis performed in step S2009 can use information inthe diagnosis case DB 1416, which will not be described in detail. Then,in step S911, the analysis center 1410 judges, from the result of thetissue structure analysis, whether analysis of a high-magnificationimage of the same ROI is necessary. If analysis of a high-magnificationimage of the same ROI is necessary, the process advances to step 913,and the analysis center 1410 requests the pathologist terminal 220serving as the transmission source to transmit high-magnification imagedata of the same ROI.

If the received image data is high-magnification image data, the processadvances to step S2015, and the analysis center 1410 performs featureanalysis of the high-magnification image data corresponding to theportion, staining method, sex/age, and the like. In the embodiment, theanalysis center 1410 generates auxiliary diagnosis information bylooking up the diagnosis case DB 1416 in step S2017. In step S2019, theanalysis center 1410 transmits the feature analysis result of thehigh-magnification image data and the auxiliary diagnosis information tothe pathologist terminal 220 serving as the transmission source togetherwith the image number obtained from the transmission source.

<<Hardware Arrangement of Pathologist Terminal>>

The hardware arrangement of the pathologist terminal is basically thesame as that in FIG. 10, and a description thereof will not be repeated.

(Patient History DB)

FIG. 21 is a chart showing the structure of a patient history DB 1052shown in FIG. 10.

In the upper view of FIG. 21, reference numeral 2101 denotes a patientID for identifying a patient; and 2102, a sex/age of a patient that isinformation for specifying a patient. Although another specifyinginformation such as the address of a patient is also stored, FIG. 21shows only information necessary for processing in the embodiment.Reference numeral 2103 denotes a portion of a tissue sample image to beanalyzed; and 2104, a staining method for the tissue that is informationassociated with an analysis method in the analysis center 1410.Reference numeral 2105 denotes an examination date & time when a targettissue sample image was obtained.

The patient history DB 1052 stores, for each examination, analysisresults 2106 from the analysis center 1410, auxiliary diagnosisinformation 2107 from the analysis center 1410, and a diagnosis result2108 by a pathologist, which are pieces of information as theexamination result.

The lower view of FIG. 21 shows information for specifying image data.Reference numeral 2111 denotes a slide ID for identifying a pathologicalslide; 2112, an analysis tissue area representing a tissue area to beanalyzed in a tissue sample image read from a pathological slide by ascanner 222; and 2113, an analysis target ROI representing an ROI to beanalyzed in a tissue area. In the embodiment, a reassigned image number2114 is registered for image data accumulated in the diagnosis case DB1416 of the analysis center 1410. Hence, image data having no reassignedimage number 2114 is not accumulated in the diagnosis case DB 1416 ofthe analysis center 1410. In this way, the presence/absence of thereassigned image number 2114 serves as a barometer of whether the imagewas used for diagnosis. Information for specifying image data isregistered in correspondence with each examination, like 2110 and 2120.

<<Operation Procedure of Pathologist Terminal>>

FIG. 22 is a flowchart showing the operation procedure of thepathologist terminal 220 according to the embodiment. A CPU 1010 in FIG.10 executes this flowchart by using a RAM 1040, thereby implementing thefunction of the pathologist terminal 220 in FIG. 14. Note that stepsS1201 to S1217 in FIG. 22 are the same as those in FIG. 12, and adescription thereof will not be repeated.

In step S2219, the pathologist terminal 220 waits for reception ofanalysis results and auxiliary diagnosis information. If the pathologistterminal 220 receives analysis results and auxiliary diagnosisinformation, the process advances to step S2221, and the pathologistterminal 220 generates a display screen by superimposing the analysisresults and auxiliary diagnosis information on a tissue area. In stepS2223, the pathologist terminal 220 displays the superimposed image (seeFIG. 17).

In step S2225, the pathologist terminal 220 waits for input of adiagnosis result by a pathologist. If the diagnosis result by thepathologist is input, the process advances to step S2227, and thepathologist terminal 220 transmits the diagnosis result to the analysiscenter 1410. In step S2229, the pathologist terminal 220 receivesreassigned image numbers from the analysis center 1410, and records themin the patient history DB 1052 of FIG. 21. Holding informationassociated with diagnosis in the analysis center 1410 together withimage data reduces the amount of data accumulated in the pathologistterminal 220 (see FIG. 21).

[Fifth Embodiment]

In the second to fifth embodiments, image data of an ROI is transmittedfrom the pathologist terminal 220 to the analysis center 210 or 1410without the mediacy of diagnosis by a pathologist. The fifth embodimentwill explain processing of requesting diagnosis assistance of ananalysis center 210 for a tissue sample image for which diagnosis by apathologist is difficult regardless of local or remote diagnosis.According to the fifth embodiment, diagnosis assistance is requested notfor all tissue sample images, but only when diagnosis by a pathologistis difficult. While reducing the burden on the analysis center 210,assistance of the analysis center for diagnosis by a pathologist basedon a tissue sample image can be quickly received at high accuracy.

The arrangements of an information processing system, analysis center,and a pathologist terminal according to the fifth embodiment are similarto those in the fourth embodiment and can be inferred, so a descriptionthereof will not be repeated.

<<Operation Sequence of Information Processing System>>

FIG. 23 is a sequence chart showing an operation sequence 2300 of apathological image diagnosis assistance system 1400 serving as theinformation processing system according to the embodiment.

First, a scanner 222 reads a pathological slide in step S2301, anddiagnosis processing by a pathologist is performed locally or remotelyin step S2303. In step S2305, it is judged whether the diagnosis isdifficult. If the diagnosis is easy, the patient is notified of thediagnosis result.

If the diagnosis is difficult, the process advances to step S2307 toselect an ROI of a tissue area, analysis of which is requested fordiagnosis assistance. This ROI selection is selection of a locationwhere judgment is difficult in diagnosis. In step S2309,high-magnification image data of the selected ROI is transmitted to theanalysis center 210 to request analysis.

Processes from analysis processing (step S1517) in the analysis center210 to display (step S1525) on a display 223 of a pathologist terminal220 are the same as those in FIG. 15, and a description thereof will notbe repeated. Even when the analysis center 210 is requested to analyze alocation where judgment is difficult, processing of the embodiment totransmit low-magnification image data first, and if necessary, transmithigh-magnification image data may be applied.

[Other Embodiments]

The above embodiments have mainly explained a case in which a cancer isdetected from a tissue sample image using the HE method as the stainingmethod. However, the present invention is further applicable to a casein which whether a cancer region is positive or negative is determinedfrom a tissue sample image immunostained by the IHC method. For example,the IHC method for a mammary duct uses, as features, the ratio of nucleistained in brown, the ratio of unstained nuclei (blue nuclei), and theentire concentric staining of a membrane (whether the entire membrane isstained). In the IHC method, whether ER/PR or Her2 is positive ornegative is judged at a fixed magnification (for example, 20×) for aregion known to have a cancer. In accordance with this result, atreatment method is selected. When a tissue sample image is remotelyanalyzed by the IHC method, it is also conceivable to transmit, to ananalysis center 210, a 20X image of a tissue sample image which isformed from serial sections for a cancer region detected from a tissuesample image obtained by the HE method, and receive a negative/positiveresult.

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all such modifications and equivalent structures andfunctions.

The present invention can be applied to a system including pluraldevices or a single apparatus. The present invention can be applied to acase in which a control program for implementing the functions of theembodiments is supplied to the system or apparatus directly or from aremote site. Hence, the control program installed in a computer toimplement the functions of the present invention by the computer, amedium storing the control program, or a WWW (World Wide Web) server todownload the control program is also incorporated in the presentinvention.

The invention claimed is:
 1. An information processing apparatus whichrequests assistance with a diagnosis based on a tissue sample imageobtained by staining and capturing a tissue, the information processingapparatus comprising: a processor, a memory, and a communicationcontroller, wherein: the communication controller comprises: a firsttransmitter that transmits, to an information apparatus assisting withthe diagnosis, lower magnification image data in association withtransmission source identifying information for identifying theinformation processing apparatus, and image data identifying informationfor identifying the image data, wherein the lower magnification imagedata is selected from among a plurality of image data obtained atdifferent magnifications for an area image selected in the tissue sampleimage; the communication controller further comprises: a secondtransmitter that transmits, to the information apparatus assisting withthe diagnosis, based on receiving a request of transmitting highermagnification image data selected from among the plurality of image dataobtained at different magnifications from the information apparatusassisting with the diagnosis, the higher magnification image data forthe area image in association with the transmission source identifyinginformation and the image data identifying information; thecommunication controller further comprises: a receiver that receives,from the information apparatus assisting with the diagnosis, featureinformation of the area image generated by the information apparatusassisting with the diagnosis based on the higher magnification data inassociation with the image data identifying information; and theprocessor comprises: a display unit that displaying presence/absenceinformation of the request for the area image, and information based onthe received feature information of the area image.
 2. A method forcontrolling an information processing apparatus which requestsassistance with a diagnosis based on a tissue sample image obtained bystaining and capturing a tissue, comprising: transmitting, to aninformation apparatus assisting with the diagnosis, lower magnificationimage data in association with transmission source identifyinginformation for identifying the information processing apparatus, andimage data identifying information for identifying the image data,wherein the lower magnification image data is selected from among aplurality of image data obtained at different magnifications for an areaimage selected in the tissue sample image; transmitting, to theinformation apparatus assisting with the diagnosis, based on receiving arequest to transmit higher magnification image data selected from amongthe plurality of image data obtained at different magnifications fromthe information apparatus assisting with the diagnosis, the highermagnification image data for the area image in association with thetransmission source identifying information and the image dataidentifying information; receiving feature information of the area imagegenerated by the information apparatus assisting with the diagnosisbased on the higher magnification image data in association with theimage data identifying information; and displaying presence/absenceinformation of the request for the area image, and information based onthe received feature information of the area image.
 3. A non-transitorycomputer-readable storage medium storing a program for controlling aninformation processing apparatus which requests assistance with adiagnosis based on a tissue sample image obtained by staining andcapturing a tissue, the control program causing a computer to execute:transmitting, to an information apparatus assisting with the diagnosis,lower magnification image data in association with transmission sourceidentifying information for identifying the information processingapparatus, and image data identifying information for identifying theimage data, wherein the lower magnification image data is selected fromamong a plurality of image data obtained at different magnifications foran area image selected in the tissue sample image; transmitting, to theinformation apparatus assisting with the diagnosis, based on receiving arequest to transmit higher magnification image data selected from amongthe plurality of image data obtained at different magnifications fromthe information apparatus assisting with the diagnosis, the highermagnification image data for the area image in association with thetransmission source identifying information and the image dataidentifying information; receiving feature information of the area imagegenerated by the information apparatus assisting with the diagnosisbased on the higher magnification image data in association with theimage data identifying information; and displaying presence/absenceinformation of the request for the area image, and information based onthe received feature information of the area image.
 4. The informationprocessing apparatus according to claim 1, wherein the image dataidentifying information comprises information specifying a portion of anorganism of the tissue sample image.
 5. The information processingapparatus according to claim 1, wherein the image data identifyinginformation comprises information specifying a staining method of thetissue sample image.
 6. The information processing apparatus accordingto claim 5, wherein the staining method comprises an HE method.
 7. Theinformation processing apparatus according to claim 1, wherein thefeature information comprises a feature of a stained cell in the areaimage.
 8. The information processing apparatus according to claim 7,wherein the feature comprises an average nucleus size, an averagenuclear grade, and a texture.
 9. The information processing apparatusaccording to claim 1, wherein the communication controller furthercomprises: a third transmitter that transmits, to the informationapparatus assisting with the diagnosis, a diagnosis result associatedwith image data of the area image, wherein said receiver furtherreceives, from the information apparatus assisting with the diagnosis,auxiliary diagnosis information generated from the image data of thearea image with reference to image data of area images in associationwith diagnosis results, and said display unit further displays theauxiliary diagnosis information.
 10. The information processingapparatus according to claim 9, wherein the auxiliary diagnosisinformation comprises at least one of information representing whether acondition of a disease is malignant or benign, an average survival time,presence/absence of metastasis, a recurrence rate, and auxiliaryinformation of a treatment plan.